Integrated methodology for state and parameter estimation of spark-ignition engines

To develop an effective control and monitoring scheme for automotive engines, a precise knowledge of the parameters and unmeasurable states of the nonlinear model capturing the overall dynamics of engines is of utmost importance. For a new vehicle out of the assembly line, the nonlinear model has constant parameters. However, in the long run, due to regular wear-and-tear, and for other unpredictable disturbances, they may change. The main challenges are how to obtain the information of parameters and states under the influence of process noise and measurement noise. To address these challenges, we present a new integrated state and parameter estimation algorithm in this paper for spark ignition (SI) engines based on the constrained unscented Kalman filter and the improved recursive least square technique. The system under consideration is a highly nonlinear mean value SI engine model consisting of the throttle, intake manifold, engine speed dynamics, and fuel system. The performance of the proposed algorithm in terms of root-mean-square-error and robustness with regards to initial conditions and random noises is analysed through exhaustive simulation scenarios considering constant, and time-varying parameters. In addition, the performance of other state-of-the-art estimation algorithms is also compared with that of the developed integrated algorithm.

[1]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[2]  Bjarne A. Foss,et al.  Applying the unscented Kalman filter for nonlinear state estimation , 2008 .

[3]  Henrique Marra Menegaz,et al.  A Systematization of the Unscented Kalman Filter Theory , 2015, IEEE Transactions on Automatic Control.

[4]  Dennis S. Bernstein,et al.  Please Scroll down for Article International Journal of Control State Estimation for Linear and Non-linear Equality-constrained Systems State Estimation for Linear and Non-linear Equality-constrained Systems , 2022 .

[5]  Anuradha M. Annaswamy,et al.  Spark ignition engine fuel-to-air ratio control: An adaptive control approach , 2010 .

[6]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[7]  Simon J. Julier,et al.  The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[8]  G. Evensen.  Data Assimilation: The Ensemble Kalman Filter , 2006 .

[9]  Eric A. Wan,et al.  Dual Extended Kalman Filter Methods , 2002 .

[10]  Lino Guzzella,et al.  Introduction to Modeling and Control of Internal Combustion Engine Systems , 2004 .

[11]  Chen-Fang Chang,et al.  Observer-based air fuel ratio control , 1998 .

[12]  Jonathan Chauvin,et al.  Combustion Parameters Estimation Based on Knock Sensor for Control Purpose Using Dedicated Signal Processing Platform , 2008 .

[13]  Yuen-Kwok Chin,et al.  Engine Dynamics: Time-Based Versus Crank-Angle Based , 1986 .

[14]  Vadim I. Utkin,et al.  Automotive engine diagnosis and control via nonlinear estimation , 1998 .

[15]  Stephen L. Cooper,et al.  Air Charge and Residual Gas Fraction Estimation for a Spark-Ignition Engine Using In-Cylinder Pressure , 2017 .

[16]  Matthew A. Franchek,et al.  Intake Air Path Diagnostics for Internal Combustion Engines , 2007 .

[17]  Brian D. O. Anderson,et al.  Exponential convergence of recursive least squares with exponential forgetting factor , 1982 .

[18]  Faa-Jeng Lin.  Application of EKF and RLS estimators in induction motor drive , 1996, PESC Record. 27th Annual IEEE Power Electronics Specialists Conference.

[19]  Aamer I. Bhatti,et al.  Estimation of Gasoline-Engine Parameters Using Higher Order Sliding Mode , 2008, IEEE Transactions on Industrial Electronics.

[20]  Feng Ding,et al.  Combined state and least squares parameter estimation algorithms for dynamic systems , 2014 .

[21]  S. S. Douglas,et al.  Adaptive neural network model based predictive control for air-fuel ratio of SI engines , 2006, Eng. Appl. Artif. Intell..

[22]  Eric Warren Curtis,et al.  A New Port and Cylinder Wall Wetting Model to Predict Transient Air/Fuel Excursions in a Port Fuel Injected Engine , 1996 .

[23]  Nicolò Cavina,et al.  Spark Advance Control based on a Grey Box Model of the Combustion Process , 2005 .

[24]  Elbert Hendricks,et al.  Mean Value Modelling of Spark Ignition Engines , 1990 .

[25]  Dennis S. Bernstein,et al.  Reduced-rank unscented Kalman filtering using Cholesky-based decomposition , 2008, 2008 American Control Conference.

[26]  Elbert Hendricks,et al.  Modelling of the Intake Manifold Filling Dynamics , 1996 .

[27]  John T. Betts,et al.  Practical Methods for Optimal Control and Estimation Using Nonlinear Programming , 2009 .

[28]  Riccardo Scattolini,et al.  Modelling the volumetric efficiency of ic engines: Parametric, non-parametric and neural techniques , 1996 .

[29]  Elbert Hendricks,et al.  A Generic Mean Value Engine Model for Spark Ignition Engines , 2000 .

[30]  Marimuthu Palaniswami,et al.  Model Predictive Control of a Fuel Injection System with a Radial Basis Function Network Observer , 2002 .

[31]  William O'Brien,et al.  Sequential state prediction and parameter estimation with constrained dual extended Kalman filter for building zone thermal responses , 2019, Energy and Buildings.

[32]  M. V. Kulikova,et al.  Accurate continuous-discrete unscented Kalman filtering for estimation of nonlinear continuous-time stochastic models in radar tracking , 2017, Signal Process..

[33]  Javad Mohammadpour,et al.  A parameter-varying filtered PID strategy for air–fuel ratio control of spark ignition engines , 2012 .

[34]  Lars Imsland,et al.  On convergence of the unscented Kalman–Bucy filter using contraction theory , 2016, Int. J. Syst. Sci..

[35]  Yong Sun,et al.  A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle׳ mass for a hybrid electric bus , 2016 .

[36]  Tushar Jain,et al.  State and Parameter Estimation for Spark Ignition Engine with Parameter Uncertainty , 2020, 2020 28th Mediterranean Conference on Control and Automation (MED).

[37]  J.A. Cook,et al.  Modeling of an internal combustion engine for control analysis , 1988, IEEE Control Systems Magazine.

[38]  Jinwu Gao,et al.  An On-Board Calibration Scheme for Map-Based Combustion Phase Control of Spark-Ignition Engines , 2017, IEEE/ASME Transactions on Mechatronics.

[39]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[40]  MengChu Zhou,et al.  Nonlinear Bayesian estimation: from Kalman filtering to a broader horizon , 2017, IEEE/CAA Journal of Automatica Sinica.

[41]  Ahmed Alsaedi,et al.  Data filtering-based parameter and state estimation algorithms for state-space systems disturbed by coloured noises , 2020, Int. J. Syst. Sci..

[42]  João Yoshiyuki Ishihara,et al.  Unscented Kalman Filters for Estimating the Position of an Automotive Electronic Throttle Valve , 2016, IEEE Transactions on Vehicular Technology.

[43]  Guido Herrmann,et al.  Adaptive air-fuel ratio control for spark ignition engines with time-varying parameter estimation , 2017, 2017 9th International Conference on Modelling, Identification and Control (ICMIC).

[44]  Michael Athans,et al.  On the design of electronic automotive engine controls using linear quadratic control theory , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[45]  Zhao Yang Dong,et al.  Adaptive and Learning Control for SI Engine Model With Uncertainties , 2009 .

[46]  Rolf Isermann,et al.  Identification of Dynamic Systems: An Introduction with Applications , 2010 .

[47]  Jovan Todorović,et al.  Prediction of Brake Pad Life-Further Development of LWH , 1986 .